Developing resilience pathways for interdependent infrastructure networks
A simulation-based approach with consideration to risk preferences of decision-makers
Bibliographic Data
| ID | 21233591 |
|---|---|
| Authors | Srijith Balakrishnan (0000-0002-5166-4283, Delft University of Technology), Lawrence Jin (0000-0003-3679-874X, National University of Singapore), Beatrice Cassottana (0000-0001-5856-9361), Alberto Costa (0000-0002-8321-5976), Giovanni Sansavini (0000-0002-8801-9667, ETH Zurich) |
| Year | 2024 |
| Volume | 115 |
| Pages | 105795 |
| Publication date | 2024-11-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Cities and Society (JOURNAL) |
| Journal identifiers | ISSN: 2210-6707 • E-ISSN: 2210-6715 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.scs.2024.105795 |
| OpenAlex | W4402726612 |
| Language | EN |
| Citations received | 4 |
| References cited | 42 |
In this study, we propose a methodological framework to identify and evaluate cost-effective pathways for enhancing resilience in large-scale interdependent infrastructure systems, considering decision-makers’ risk preferences. We focus on understanding how decision-makers with varying risk preferences perceive the benefits from infrastructure resilience investments and compare them with upfront costs in the context of high-impact low-probability (HILP) events. First, we compute the costs of interventions as the sum of their capital costs and maintenance costs. The benefits of the interventions include the reduction in physical damage costs and business disruption losses resulting from the improved resilience of the network. In the final stage, we develop statistical models to predict the perceived net benefits of different network resilience configurations in power, water, and transport networks. These models are employed in an optimization framework to identify optimal resilience investment pathways. By incorporating Cumulative Prospect Theory (CPT) in the optimization framework, we show that decision-makers who assign higher weights to low probability events tend to allocate more resources towards post-disaster recovery strategies leading to increased resilience against HILP events, like earthquakes. We illustrate the methodology using a case study of the interdependent infrastructure network in Shelby County, Tennessee
Business · Environmental planning · Environmental resource management · Interdependence · Management science · Sociology · Computer Science · Disaster Management and Resilience · Engineering · Environmental Science · Infrastructure Resilience and Vulnerability Analysis · Risk and Safety Analysis
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| Citations per year | 4 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |